“Belonging analytics”: A proposal

Abstract

Students’ sense of belonging is associated with successful transition into higher education and a range of positive outcomes including enhanced learning, well-being, and demonstrated achievement. The COVID-19 pandemic underscored the importance of belonging as the shift to online learning highlighted the challenges of supporting and monitoring student belonging. Attending to belonging is not simple, however; students’ experiences with belonging are complex, dynamic, and contextual. In creating a new agenda connecting the fields of belonging and learning analytics, we propose the idea of “belonging analytics” to address the challenge of supporting and tracking students’ belonging. In this paper, we discuss how the understanding of belonging may be enhanced through learning analytics. Advancements in learning analytics, such as digital trace data, narratives, textual data, or a combination, may be harnessed to gain insights into ongoing experience of belonging, and consequently to support belonging. We conclude with a set of open questions to interested researchers and practitioners, to advance the field of belonging analytics. LIFT Learning: The authors recently presented on the topic of Belonging Analytics at Indiana University’s 5th International Learning Analytics Summit. Engage with part of this discussion through this article’s companion LIFT Learning site where the authors describe their proposition and lay out the key concepts and challenges associated with belonging. As part of this, the authors discuss the case study presented in the article in greater detail, and provide additional contextual information that enhances the reader’s understanding of the proposal. The LIFT Learning site is available at https://apps.lift.c3l.ai/learning/course/course-v1:LEARNINGLETTERS+0104+2023